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Lech, Malte [VerfasserIn]; Ghaffarian, Saman [VerfasserIn]; Kerle, Norman [VerfasserIn]; Leppert, Gerald [VerfasserIn]; Nawrotzki, Raphael [VerfasserIn]; Moull, Kevin [VerfasserIn]; Harten, Sven [VerfasserIn] ; Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit (DEval)

A Proof-of-Concept of Integrating Machine Learning, Remote Sensing, and Survey Data in Evaluations: The Measurement of Disaster Resilience in the Philippines

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  • Medientyp: E-Book
  • Titel: A Proof-of-Concept of Integrating Machine Learning, Remote Sensing, and Survey Data in Evaluations: The Measurement of Disaster Resilience in the Philippines
  • Beteiligte: Lech, Malte [VerfasserIn]; Ghaffarian, Saman [VerfasserIn]; Kerle, Norman [VerfasserIn]; Leppert, Gerald [VerfasserIn]; Nawrotzki, Raphael [VerfasserIn]; Moull, Kevin [VerfasserIn]; Harten, Sven [VerfasserIn]
  • Körperschaft:
  • Erschienen: Bonn, 2020
  • Erschienen in: DEval Discussion Papers ; Bd. 1/2020
  • Umfang: 28 S.
  • Sprache: Englisch
  • ISBN: 9783961261147
  • Identifikator:
  • Schlagwörter: Datengewinnung ; Südostasien ; Klimawandel ; Philippinen ; Algorithmus ; Risikomanagement ; Krisenmanagement ; Naturkatastrophe ; sozioökonomische Faktoren ; remote sensing ; machine learning ; Fernerkundung
  • Entstehung:
  • Anmerkungen: Erstveröffentlichung
    begutachtet (peer reviewed)
  • Beschreibung: Disaster resilience is a topic of increasing importance for policy makers in the context of climate change. However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0-2 years) compared to the late recovery phase (2-3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of-concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales.
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung - Nicht-kommerziell - Keine Bearbeitung (CC BY-NC-ND)